A.J. Oldehinkel (Albertine)http://repub.eur.nl/ppl/17770/
List of Publicationsenhttp://repub.eur.nl/eur_signature.pnghttp://repub.eur.nl/
RePub, Erasmus University RepositoryMental health in Dutch adolescents: a TRAILS report on prevalence, severity, age of onset, continuity and co-morbidity of DSM disordershttp://repub.eur.nl/pub/65492/
Fri, 20 Jun 2014 00:00:01 GMT<div>J. Ormel</div><div>D. Raven</div><div>F.V.A. van Oort</div><div>C.A. Hartman</div><div>S.A. Reijneveld</div><div>R. Veenstra</div><div>W.A.M. Vollebergh</div><div>J.K. Buitelaar</div><div>F.C. Verhulst</div><div>A.J. Oldehinkel</div>
Background: With psychopathology rising during adolescence and evidence suggesting that adult mental health burden is often due to disorders beginning in youth, it is important to investigate the epidemiology of adolescent mental disorders. Method: We analysed data gathered at ages 11 (baseline) and 19 years from the population-based Dutch TRacking Adolescents' Individual Lives Survey (TRAILS) study. At baseline we administered the Achenbach measures (Child Behavior Checklist, Youth Self-Report) and at age 19 years the World Health Organization's Composite International Diagnostic Interview version 3.0 (CIDI 3.0) to 1584 youths. Results: Lifetime, 12-month and 30-day prevalences of any CIDI-DSM-IV disorder were 45, 31 and 15%, respectively. Half were severe. Anxiety disorders were the most common but the least severe whereas mood and behaviour disorders were less prevalent but more severe. Disorders persisted, mostly by recurrence in mood disorders and chronicity in anxiety disorders. Median onset age varied substantially across disorders. Having one disorder increased subjects' risk of developing another disorder. We found substantial homotypic and heterotypic continuity. Baseline problems predicted the development of diagnosable disorders in adolescence. Non-intact families and low maternal education predicted externalizing disorders. Most morbidity concentrated in 5-10% of the sample, experiencing 34-55% of all severe lifetime disorders. Conclusions: At late adolescence, 22% of youths have experienced a severe episode and 23% only mild episodes. This psychopathology is rather persistent, mostly due to recurrence, showing both monotypic and heterotypic continuity, with family context affecting particularly externalizing disorders. High problem levels at age 11 years are modest precursors of incident adolescent disorders. The burden of mental illness concentrates in 5-10% of the adolescent population. CopyrightChildhood adversities and adolescent depression: A matter of both risk and resiliencehttp://repub.eur.nl/pub/54307/
Mon, 16 Jun 2014 00:00:01 GMT<div>A.J. Oldehinkel</div><div>J. Ormel</div><div>F.C. Verhulst</div><div>E. Nederhof</div>
Childhood adversities have been proposed to modify later stress sensitivity and risk of depressive disorder in several ways: by stress sensitization, stress amplification, and stress inoculation. Combining these models, we hypothesized that childhood adversities would increase risk of early, but not later, onsets of depression (Hypothesis 1). In those without an early onset, childhood adversities were hypothesized to predict a relatively low risk of depression in high-stress conditions (Hypothesis 2a) and a relatively high risk of depression in low-stress conditions (Hypothesis 2b), compared to no childhood adversities. These hypotheses were tested in 1,584 participants of the Tracking Adolescents' Individual Lives Survey, a prospective cohort study of adolescents. Childhood adversities were assessed retrospectively at ages 11 and 13.5, using self-reports and parent reports. Lifetime DSM-IV major depressive episodes were assessed at age 19, by means of the Composite International Diagnostic Interview. Stressful life events during adolescence were established using interview-based contextual ratings of personal and network events. The results provided support for all hypotheses, regardless of the informant and timeframe used to assess childhood adversities and regardless of the nature (personal vs. network, dependent vs. independent) of recent stressful events. These findings suggest that age at first onset of depression may be an effective marker to distinguish between various types of reaction patterns. CopyrightFrom positive emotionality to internalizing problems: the role of executive functioning in preschoolershttp://repub.eur.nl/pub/61548/
Sun, 13 Apr 2014 00:00:01 GMT<div>A. Ghassabian</div><div>E. Székely</div><div>C.M. Herba</div><div>V.W.V-K. Jaddoe</div><div>A. Hofman</div><div>A.J. Oldehinkel</div><div>F.C. Verhulst</div><div>H.W. Tiemeier</div>
Temperament and psychopathology are intimately related; however, research on the prospective associations between positive emotionality, defined as a child's positive mood states and high engagement with the environment, and psychopathology is inconclusive. We examined the longitudinal relation between positive emotionality and internalizing problems in young children from the general population. Furthermore, we explored whether executive functioning mediates any observed association. Within a population-based Dutch birth cohort, we observed positive emotionality in 802 children using the laboratory temperament assessment battery at age 3 years. Child behavior checklist (CBCL) internalizing problems (consisting of Emotionally Reactive, Anxious/Depressed, and Withdrawn scales) were assessed at age 6 years. Parents rated their children's executive functioning at ages 4 years. Children with a lower positive emotionality at age 3 had a higher risk of withdrawn problems at age 6 years (OR = 1.20 per SD decrease in positive emotionality score, 95 % CI: 1.01, 1.42). This effect was not explained by preexisting internalizing problems. This association was partly mediated by more problems in the shifting domain of executive functioning (p < 0.001). We did not find any relation between positive emotionality and the CBCL emotionally reactive or anxious/depressed scales. Although the effect sizes were moderate, our results suggest that low levels of positive emotionality at preschool age can result in children's inflexibility and rigidity later in life. The inflexibility and rigidity are likely to affect the child's drive to engage with the environment, and thereby lead to withdrawn problems. Further research is needed to replicate these findings.Glucocorticoid receptor gene (NR3C1) methylation following stressful events between birth and adolescence. the TRAILS studyhttp://repub.eur.nl/pub/57506/
Tue, 08 Apr 2014 00:00:01 GMT<div>L.J. van der Knaap</div><div>H. Riese</div><div>J.J. Hudziak</div><div>M. Verbiest</div><div>F.C. Verhulst</div><div>A.J. Oldehinkel</div><div>F.V.A. van Oort</div>
Stress early in life is a known risk factor for the development of affective disorders later in life. Epigenetic mechanisms, such as DNA methylation, may have an important role in mediating that risk. Recent epigenetic research reported on the long-term relationship between traumatic stress in childhood and DNA methylation in adulthood. In this study, we examined the impact of various types of stress (perinatal stress, stressful life events (SLEs) and traumatic youth experiences) on methylation of the glucocorticoid receptor gene (NR3C1) in the blood of a population sample of 468 adolescents (50.4% female, mean age 16.1 years). Second, we determined whether stress at different ages was associated with higher NR3C1 methylation. NR3C1 methylation rates were higher after exposure to SLEs and after exposure to traumatic youth experiences. NR3C1 methylation in adolescence was not higher after exposure to perinatal stress. Experience of SLEs in adolescence was associated with a higher NR3C1 methylation, independently of childhood SLEs. We demonstrate that not only traumatic youth experiences but also (more common) SLEs are associated with higher NR3C1 methylation. In addition, our findings underline the relevance of adolescent stress for epigenetic changes in the NR3C1 gene.Gene-centric meta-analysis in 87,736 individuals of European ancestry identifies multiple blood-pressure-related locihttp://repub.eur.nl/pub/55613/
Thu, 06 Mar 2014 00:00:01 GMT<div>V. Tragante</div><div>M. Barnes</div><div>S.K. Ganesh</div><div>M.B. Lanktree</div><div>W. Guo</div><div>N. Franceschini</div><div>G.D. Smith</div><div>T. Johnson</div><div>M.V. Holmes</div><div>S. Padmanabhan</div><div>K.J. Karczewski</div><div>B. Almoguera</div><div>J. Barnard</div><div>J. Baumert</div><div>Y.-P.C. Chang</div><div>C.C. Elbers</div><div>M. Farrall</div><div>M.E. Fischer</div><div>T.R. Gaunt</div><div>J.M.I.H. Gho</div><div>C. Gieger</div><div>A. Goel</div><div>Y. Gong</div><div>A.J. Isaacs</div><div>M.E. Kleber</div><div>I.M. Leach</div><div>C.W. McDonough</div><div>M.F.L. Meijs</div><div>O. Melander</div><div>C.P. Nelson</div><div>M. Nolte</div><div>V.S. Pankratz</div><div>T.S. Price</div><div>J. Shaffer</div><div>S. Shah</div><div>M. Tomaszewski</div><div>P.J. van der Most</div><div>E.P.A. van Iperen</div><div>J.M. Vonk</div><div>H.E. Witkowska</div><div>C.O.L. Wong</div><div>L. Zhang</div><div>A.L. Beitelshees</div><div>G. Berenson</div><div>D.L. Bhatt</div><div>M.J. Brown</div><div>A.D. Burt</div><div>R.M. Cooper-Dehoff</div><div>J. Connell</div><div>K.J. Cruickshanks</div><div>S.P. Curtis</div><div>G. Davey-Smith</div><div>C. Delles</div><div>R.T. Gansevoort</div><div>X. Guo</div><div>S. Haiqing</div><div>C.E. Hastie</div><div>M.A. Hofker</div><div>G.K. Hovingh</div><div>D.S. Kim</div><div>S.A. Kirkland</div><div>B.E.K. Klein</div><div>B.E.K. Klein</div><div>Y.R. Li</div><div>R. Maiwald</div><div>C. Newton-Cheh</div><div>E. O'Brien</div><div>N.C. Onland-Moret</div><div>W. Palmas</div><div>A. Parsa</div><div>B.W.J.H. Penninx</div><div>M. Pettinger</div><div>R.S. Vasan</div><div>J.E. Ranchalis</div><div>P. M Ridker</div><div>L.M. Rose</div><div>P. Sever</div><div>D. Shimbo</div><div>L. Steele</div><div>R.P. Stolk</div><div>B. Thorand</div><div>M.D. Trip</div><div>C.M. van Duijn</div><div>W.M.M. Verschuren</div><div>C. Wijmenga</div><div>S. Wyatt</div><div>J.C. Young</div><div>A.H. Zwinderman</div><div>C.R. Bezzina</div><div>E. Boerwinkle</div><div>J.P. Casas</div><div>M. Caulfield</div><div>A. Chakravarti</div><div>D.I. Chasman</div><div>K.W. Davidson</div><div>P.A. Doevendans</div><div>A. Dominiczak</div><div>G.A. Fitzgerald</div><div>J.G. Gums</div><div>M. Fornage</div><div>H. Hakonarson</div><div>H. van Halder</div><div>H.L. Hillege</div><div>T. Illig</div><div>G.P. Jarvik</div><div>J.A. Johnson</div><div>J.J.P. Kastelein</div><div>W. Koenig</div><div>M. Kumari</div><div>W. März</div><div>S.S. Murray</div><div>J.R. O'Connell</div><div>A.J. Oldehinkel</div><div>J.S. Pankow</div><div>D.J. Rader</div><div>S. Redline</div><div>M.P. Reilly</div><div>E.E. Schadt</div><div>K. Kottke-Marchant</div><div>H. Snieder</div><div>M. Snyder</div><div>A. Stanton</div><div>M.D. Tobin</div><div>A.G. Uitterlinden</div><div>P. van der Harst</div><div>Y.T. Schouw</div><div>N.J. Samani</div><div>H. Watkins</div><div>A.D. Johnson</div><div>A.P. Reiner</div><div>X. Zhu</div><div>P.I.W. de Bakker</div><div>D. Levy</div><div>F.W. Asselbergs</div><div>P. Munroe</div><div>J. Keating</div>
Blood pressure (BP) is a heritable risk factor for cardiovascular disease. To investigate genetic associations with systolic BP (SBP), diastolic BP (DBP), mean arterial pressure (MAP), and pulse pressure (PP), we genotyped ∼50,000 SNPs in up to 87,736 individuals of European ancestry and combined these in a meta-analysis. We replicated findings in an independent set of 68,368 individuals of European ancestry. Our analyses identified 11 previously undescribed associations in independent loci containing 31 genes including PDE1A, HLA-DQB1, CDK6, PRKAG2, VCL, H19, NUCB2, RELA, HOXC@ complex, FBN1, and NFAT5 at the Bonferroni-corrected array-wide significance threshold (p < 6 × 10-7) and confirmed 27 previously reported associations. Bioinformatic analysis of the 11 loci provided support for a putative role in hypertension of several genes, such as CDK6 and NUCB2. Analysis of potential pharmacological targets in databases of small molecules showed that ten of the genes are predicted to be a target for small molecules. In summary, we identified previously unknown loci associated with BP. Our findings extend our understanding of genes involved in BP regulation, which may provide new targets for therapeutic intervention or drug response stratification.Anxiety and disruptive behavior mediate pathways from attention-deficit/ hyperactivity disorder to depressionhttp://repub.eur.nl/pub/62917/
Sat, 01 Feb 2014 00:00:01 GMT<div>A. Roy</div><div>A.J. Oldehinkel</div><div>F.C. Verhulst</div><div>J. Ormel</div><div>C.A. Hartman</div>
Objective: The progression to depression in children with attention-deficit/hyperactivity disorder (ADHD) is not clearly understood. To clarify this relationship, we tested the following hypotheses in a population-based study: (1) children with ADHD have a higher risk of developing depression than children without ADHD; (2) the pathway from ADHD to depression is mediated (partly) through anxiety and disruptive behavior disorders; and (3) mediation through anxiety is more prevalent in girls, and mediation through disruptive behavior disorders is more prevalent in boys. Method: From October 2008 to September 2010, the Composite International Diagnostic Interview was used to assess ADHD, major depressive episodes, anxiety disorders, and disruptive behavior disorders in 1,584 participants from the TRacking Adolescents' Individual Lives Survey (TRAILS) cohort. Cox regression was used to model the effects of ADHD, anxiety, and disruptive behaviors on depression. Risk of and pathways to depression were studied in both children with ADHD and children with subthreshold ADHD. Results: Comorbid depression was present in 36% of children with a diagnosis of ADHD, 24% of children with subthreshold ADHD, and 14% of children with no ADHD. Anxiety and disruptive behaviors mediated 32% of depression in ADHD. Pathways through anxiety and disruptive behavior disorders were independent of gender. Disruptive behavior disorder was a stronger mediator than anxiety for both genders (all P < .01). Conclusions: These findings may help forewarn of impending depression and therefore allow opportunities for interventions when comorbid anxiety and/or disruptive behavior disorders are present in a child with ADHD.Physical activity and onset of depression in adolescents: A prospective study in the general population cohort TRAILShttp://repub.eur.nl/pub/67095/
Tue, 01 Oct 2013 00:00:01 GMT<div>N. Stavrakakis</div><div>A.M. Roest</div><div>F.C. Verhulst</div><div>J. Ormel</div><div>P.J.F. de Jonge</div><div>A.J. Oldehinkel</div>
Although it has often been suggested that physical activity and depression are intertwined, only few studies have investigated whether specific aspects of physical activity predict the incidence of major depression in adolescents from the general population. Therefore the aim of this study was to investigate the effects of nature, frequency, duration and intensity of physical activity during early adolescence on the onset of a major depressive episode in early adulthood. In a population sample of adolescents (N=1396), various aspects of physical activity were assessed at early adolescence (mean age 13.02, SD=0.61). Major depressive episode onset was assessed using the Composite International Diagnostic Interview. A Cox regression model was performed to investigate whether physical activity characteristics and their interactions with gender predicted a major depressive episode onset up until mean age 18.5 (SD=0.61). The individual characteristics of physical activity (nature, frequency, duration and intensity) or their interactions with gender did not predict a major depressive episode onset (p values >0.05). So far, there is no prospective evidence that physical activity protects against the development of adolescent depressive episodes in either boys or girls.Effects of structural and dynamic family characteristics on the development of depressive and aggressive problems during adolescence. The TRAILS studyhttp://repub.eur.nl/pub/72971/
Tue, 17 Sep 2013 00:00:01 GMT<div>S.J. Sijtsema</div><div>A.J. Oldehinkel</div><div>R. Veenstra</div><div>F.C. Verhulst</div><div>J. Ormel</div>
Both structural (i.e., SES, familial psychopathology, family composition) and dynamic (i.e., parental warmth and rejection) family characteristics have been associated with aggressive and depressive problem development. However, it is unclear to what extent (changes in) dynamic family characteristics have an independent effect on problem development while accounting for stable family characteristics and comorbid problem development. This issue was addressed by studying problem development in a large community sample (N = 2,230; age 10-20) of adolescents using Linear Mixed models. Paternal and maternal warmth and rejection were assessed via the Egna Minnen Beträffande Uppfostran for Children (EMBU-C). Aggressive and depressive problems were assessed via subscales of the Youth/Adult Self-Report. Results showed that dynamic family characteristics independently affected the development of aggressive problems. Moreover, maternal rejection in preadolescence and increases in paternal rejection were associated with aggressive problems, whereas decreases in maternal rejection were associated with decreases in depressive problems over time. Paternal and maternal warmth in preadolescence was associated with fewer depressive problems during adolescence. Moreover, increases in paternal warmth were associated with fewer depressive problems over time. Aggressive problems were a stable predictor of depressive problems over time. Finally, those who increased in depressive problems became more aggressive during adolescence, whereas those who decreased in depressive problems became also less aggressive. Besides the effect of comorbid problems, problem development is to a large extent due to dynamic family characteristics, and in particular to changes in parental rejection, which leaves much room for parenting-based interventions.Plasticity genes do not modify associations between physical activity and depressive symptomshttp://repub.eur.nl/pub/72574/
Mon, 01 Jul 2013 00:00:01 GMT<div>N. Stavrakakis</div><div>A.J. Oldehinkel</div><div>E. Nederhof</div><div>R.C. Oude Voshaar</div><div>T. Voshaar</div><div>J. Ormel</div><div>P.J.F. de Jonge</div>
Objective: Physical activity is inversely associated with depression in adolescents, but the overall associations are fairly weak, suggesting individual differences in the strength of the associations. The aim of this study was to investigate whether plasticity genes modify the reciprocal prospective associations between physical activity and depressive symptoms found previously. Methods: In a prospective population-based study (N 1,196), physical activity and depressive symptoms were assessed three times, around the ages of 11, 13.5, and 16. Structural Equation Modeling was used to examine reciprocal effects of physical activity and depressive symptoms over time. The plasticity genes examined were 5-HTTLPR, DRD2, DRD4, MAOA, TPH1, 5-HTR2A, COMT, and BDNF . A cumulative gene plasticity index consisting of three groups (low, intermediate, and high) according to the number of plasticity alleles carried by the adolescents was created. Using a multigroup approach, we examined whether the associations between physical activity and depressive symptoms differed between the three cumulative plasticity groups, as well as between the individual polymorphisms. Results: We found significant cross-sectional and cross-lagged paths from physical activity to depressive symptoms and vice versa. Neither the cumulative plasticity index nor the individual polymorphisms modified the strengths of these associations. Conclusion: Associations between adolescents' physical activity and depressive symptoms are not modified by plasticity genes.Genome-wide meta-analysis identifies 11 new loci for anthropometric traits and provides insights into genetic architecturehttp://repub.eur.nl/pub/57401/
Wed, 01 May 2013 00:00:01 GMT<div>S.I. Berndt</div><div>S. Gustafsson</div><div>R. Mägi</div><div>A. Ganna</div><div>E. Wheeler</div><div>M.F. Feitosa</div><div>A.E. Justice</div><div>K.L. Monda</div><div>D.C. Croteau-Chonka</div><div>F.R. Day</div><div>T. Esko</div><div>M. Fall</div><div>T. Ferreira</div><div>D. Gentilini</div><div>A.U. Jackson</div><div>J. Luan</div><div>J.C. Randall</div><div>S. Vedantam</div><div>C.J. Willer</div><div>T. Winkler</div><div>A.R. Wood</div><div>T. Workalemahu</div><div>Y.-J. Hu</div><div>S.H. Lee</div><div>L. Liang</div><div>D.Y. Lin</div><div>J. Min</div><div>M.C. Neale</div><div>G. Thorleifsson</div><div>J. Yang</div><div>E. Albrecht</div><div>N. Amin</div><div>J.L. Bragg-Gresham</div><div>G. Cadby</div><div>M. den Heijer</div><div>N. Eklund</div><div>K. Fischer</div><div>A. Goel</div><div>J.J. Hottenga</div><div>J.E. Huffman</div><div>I. Jarick</div><div>A. Johansson</div><div>T. Johnson</div><div>S. Kanoni</div><div>M.E. Kleber</div><div>I.R. König</div><div>K. Kristiansson</div><div>Z. Kutalik</div><div>C. Lamina</div><div>C. Lecoeur</div><div>G. Li</div><div>M. Mangino</div><div>W.L. McArdle</div><div>C. Medina-Gomez</div><div>M. Müller-Nurasyid</div><div>J.S. Ngwa</div><div>M. Nolte</div><div>L. Paternoster</div><div>S. Pechlivanis</div><div>M. Perola</div><div>M.J. Peters</div><div>M. Preuss</div><div>L.M. Rose</div><div>J. Shi</div><div>D. Shungin</div><div>G.D. Smith</div><div>R.J. Strawbridge</div><div>I. Surakka</div><div>A. Teumer</div><div>M.D. Trip</div><div>J.P. Tyrer</div><div>J.V. van Vliet-Ostaptchouk</div><div>L. Vandenput</div><div>L. Waite</div><div>J.H. Zhao</div><div>D. Absher</div><div>F.W. Asselbergs</div><div>M. Atalay</div><div>A.P. Attwood</div><div>A.J. Balmforth</div><div>D.C.G. Basart</div><div>J.P. Beilby</div><div>L.L. Bonnycastle</div><div>P. Brambilla</div><div>M. Bruinenberg</div><div>H. Campbell</div><div>D.I. Chasman</div><div>P.S. Chines</div><div>F.S. Collins</div><div>J. Connell</div><div>W. O Cookson</div><div>U. de Faire</div><div>F. de Vegt</div><div>M. Dei</div><div>M. Dimitriou</div><div>T. Edkins</div><div>K. Estrada Gil</div><div>D.M. Evans</div><div>M. Farrall</div><div>F. Ferrario</div><div>J. Ferrières</div><div>L. Franke</div><div>F. Frau</div><div>P.V. Gejman</div><div>H. Grallert</div><div>H. Grönberg</div><div>V. Gudnason</div><div>A. Hall</div><div>A.S. Hall</div><div>A.L. Hartikainen</div><div>C. Hayward</div><div>N.L. Heard-Costa</div><div>A.C. Heath</div><div>J. Hebebrand</div><div>G. Homuth</div><div>F.B. Hu</div><div>S.E. Hunt</div><div>E. Hyppönen</div><div>C. Iribarren</div><div>K.B. Jacobs</div><div>J.-O. Jansson</div><div>A. Jula</div><div>M. Kähönen</div><div>S. Kathiresan</div><div>F. Kee</div><div>K-T. Khaw</div><div>M. Kivimaki</div><div>W. Koenig</div><div>A. Kraja</div><div>M. Kumari</div><div>K. Kuulasmaa</div><div>J. Kuusisto</div><div>J. Laitinen</div><div>T.A. Lakka</div><div>C. Langenberg</div><div>L.J. Launer</div><div>L. Lind</div><div>J. Lindstrom</div><div>J. Liu</div><div>A. Liuzzi</div><div>M.L. Lokki</div><div>M. Lorentzon</div><div>P.A. Madden</div><div>P.K. Magnusson</div><div>P. Manunta</div><div>D. Marek</div><div>W. März</div><div>I.M. Leach</div><div>B. McKnight</div><div>S.E. Medland</div><div>E. Mihailov</div><div>L. Milani</div><div>G.W. Montgomery</div><div>V. Mooser</div><div>T.W. Mühleisen</div><div>P. Munroe</div><div>A.W. Musk</div><div>N. Narisu</div><div>G. Navis</div><div>G. Nicholson</div><div>C. Nohr</div><div>K. Ong</div><div>B.A. Oostra</div><div>C.N.A. Palmer</div><div>A. Palotie</div><div>J. Peden</div><div>N. Pedersen</div><div>A. Peters</div><div>O. Polasek</div><div>A. Pouta</div><div>P.P. Pramstaller</div><div>I. Prokopenko</div><div>C. Pütter</div><div>A. Radhakrishnan</div><div>O. Raitakari</div><div>A. Rendon</div><div>F. Rivadeneira Ramirez</div><div>I. Rudan</div><div>T. Saaristo</div><div>J.G. Sambrook</div><div>A.R. Sanders</div><div>S. Sanna</div><div>J. Saramies</div><div>S. Schipf</div><div>S. Schreiber</div><div>H. Schunkert</div><div>S.-Y. Shin</div><div>S. Signorini</div><div>J. Sinisalo</div><div>B. Skrobek</div><div>N. Soranzo</div><div>A. Stancáková</div><div>K. Stark</div><div>J. Stephens</div><div>K. Stirrups</div><div>R.P. Stolk</div><div>M. Stumvoll</div><div>A.J. Swift</div><div>E.V. Theodoraki</div><div>B. Thorand</div><div>D.-A. Tregouet</div><div>E. Tremoli</div><div>M.M. van der Klauw</div><div>J.B.J. van Meurs</div><div>S.H.H.M. Vermeulen</div><div>J. Viikari</div><div>J. Virtamo</div><div>V. Vitart</div><div>G. Waeber</div><div>Z. Wang</div><div>E. Widen</div><div>S.H. Wild</div><div>G.A.H.M. Willemsen</div><div>B. Winkelmann</div><div>J.C.M. Witteman</div><div>B.H.R. Wolffenbuttel</div><div>A. Wong</div><div>A.F. Wright</div><div>M.C. Zillikens</div><div>P. Amouyel</div><div>B.O. Boehm</div><div>E. Boerwinkle</div><div>D.I. Boomsma</div><div>M. Caulfield</div><div>S.J. Chanock</div><div>L.A. Cupples</div><div>D. Cusi</div><div>G.V. Dedoussis</div><div>J. Erdmann</div><div>J.G. Eriksson</div><div>P.W. Franks</div><div>P. Froguel</div><div>C. Gieger</div><div>U. Gyllensten</div><div>A. Hamsten</div><div>T.B. Harris</div><div>C. Hengstenberg</div><div>A.A. Hicks</div><div>A. Hingorani</div><div>A. Hinney</div><div>A. Hofman</div><div>G.K. Hovingh</div><div>K. Hveem</div><div>T. Illig</div><div>M.R. Jarvelin</div><div>K.-H. Jöckel</div><div>S. Keinanen-Kiukaanniemi</div><div>L.A.L.M. Kiemeney</div><div>D. Kuh</div><div>M. Laakso</div><div>T. Lehtimäki</div><div>D.F. Levinson</div><div>N.G. Martin</div><div>A. Metspalu</div><div>A.D. Morris</div><div>M.S. Nieminen</div><div>I. Njølstad</div><div>C. Ohlsson</div><div>A.J. Oldehinkel</div><div>W.H. Ouwehand</div><div>C. Palmer</div><div>B.W.J.H. Penninx</div><div>C. Power</div><div>M.A. Province</div><div>B.M. Psaty</div><div>L. Qi</div><div>R. Rauramaa</div><div>P.M. Ridker</div><div>S. Ripatti</div><div>V. Salomaa</div><div>N.J. Samani</div><div>H. Snieder</div><div>H.G. Sorensen</div><div>T.D. Spector</div><div>J-A. Zwart</div><div>A. Tönjes</div><div>J. Tuomilehto</div><div>A.G. Uitterlinden</div><div>M. Uusitupa</div><div>P. van der Harst</div><div>P. Vollenweider</div><div>H. Wallaschofski</div><div>N.J. Wareham</div><div>H. Watkins</div><div>H.E. Wichmann</div><div>J.F. Wilson</div><div>G.R. Abecasis</div><div>T.L. Assimes</div><div>I. Barroso</div><div>M. Boehnke</div><div>I.B. Borecki</div><div>P. Deloukas</div><div>C. Fox</div><div>T.M. Frayling</div><div>L. Groop</div><div>T. Haritunian</div><div>I.M. Heid</div><div>D. Hunter</div><div>R.C. Kaplan</div><div>F. Karpe</div><div>M.F. Moffatt</div><div>K.L. Mohlke</div><div>J.R. O´Connell</div><div>Y. Pawitan</div><div>E.E. Schadt</div><div>D. Schlessinger</div><div>V. Steinthorsdottir</div><div>D.P. Strachan</div><div>U. Thorsteinsdottir</div><div>C.M. van Duijn</div><div>P.M. Visscher</div><div>A.M. Di Blasio</div><div>J.N. Hirschhorn</div><div>C.M. Lindgren</div><div>A.D. Morris</div><div>D. Meyre</div><div>A. Scherag</div><div>M.I. McCarthy</div><div>E.K. Speliotes</div><div>K.E. North</div><div>R.J.F. Loos</div><div>E. Ingelsson</div>
Approaches exploiting trait distribution extremes may be used to identify loci associated with common traits, but it is unknown whether these loci are generalizable to the broader population. In a genome-wide search for loci associated with the upper versus the lower 5th percentiles of body mass index, height and waist-to-hip ratio, as well as clinical classes of obesity, including up to 263,407 individuals of European ancestry, we identified 4 new loci (IGFBP4, H6PD, RSRC1 and PPP2R2A) influencing height detected in the distribution tails and 7 new loci (HNF4G, RPTOR, GNAT2, MRPS33P4, ADCY9, HS6ST3 and ZZZ3) for clinical classes of obesity. Further, we find a large overlap in genetic structure and the distribution of variants between traits based on extremes and the general population and little etiological heterogeneity between obesity subgroups.Loci influencing blood pressure identified using a cardiovascular gene-centric arrayhttp://repub.eur.nl/pub/55987/
Mon, 01 Apr 2013 00:00:01 GMT<div>S.K. Ganesh</div><div>V. Tragante</div><div>W. Guo</div><div>Y. Guo</div><div>M.B. Lanktree</div><div>G.D. Smith</div><div>T. Johnson</div><div>B.A. Castillo</div><div>J. Barnard</div><div>J. Baumert</div><div>Y.-P.C. Chang</div><div>C.C. Elbers</div><div>M. Farrall</div><div>M.E. Fischer</div><div>N. Franceschini</div><div>T.R. Gaunt</div><div>J.M.I.H. Gho</div><div>C. Gieger</div><div>Y. Gong</div><div>A.J. Isaacs</div><div>M.E. Kleber</div><div>I.M. Leach</div><div>C.W. McDonough</div><div>M.F.L. Meijs</div><div>L. Mellander</div><div>C. Molony</div><div>M. Nolte</div><div>S. Padmanabhan</div><div>T.S. Price</div><div>R. Rajagopalan</div><div>J. Shaffer</div><div>S. Shah</div><div>H. Shen</div><div>N. Soranzo</div><div>P.J. van der Most</div><div>E.P.A. van Iperen</div><div>J. van Setten</div><div>J.M. Vonk</div><div>L. Zhang</div><div>A.L. Beitelshees</div><div>G. Berenson</div><div>D.L. Bhatt</div><div>J.M. Boer</div><div>E. Boerwinkle</div><div>B. Burkley</div><div>A.D. Burt</div><div>A. Chakravarti</div><div>W. Chen</div><div>R.M. Cooper-Dehoff</div><div>S.P. Curtis</div><div>A.W. Dreisbach</div><div>C. Duggan</div><div>G.B. Ehret</div><div>R.R. Fabsitz</div><div>M. Fornage</div><div>E.R. Fox</div><div>L.I. Furlong</div><div>R.T. Gansevoort</div><div>M.A. Hofker</div><div>G.K. Hovingh</div><div>S.A. Kirkland</div><div>K. Kottke-Marchant</div><div>A. Kutlar</div><div>A.Z. LaCroix</div><div>T. Langaee</div><div>Y.R. Li</div><div>H. Lin</div><div>K.Y. Liu</div><div>R. Maiwald</div><div>R. Malik</div><div>G. Murugesan</div><div>C. Newton-Cheh</div><div>J.R. O´Connell</div><div>N.C. Onland-Moret</div><div>W.H. Ouwehand</div><div>W. Palmas</div><div>B.W.J.H. Penninx</div><div>C.J. Pepine</div><div>M. Pettinger</div><div>J.F. Polak</div><div>V.S. Ramachandran</div><div>J.E. Ranchalis</div><div>S. Redline</div><div>P.M. Ridker</div><div>L.M. Rose</div><div>H. Scharnag</div><div>N.J. Schork</div><div>D. Shimbo</div><div>A.R. Shuldiner</div><div>S.R. Srinivasan</div><div>R.P. Stolk</div><div>H.A. Taylor</div><div>B. Thorand</div><div>M.D. Trip</div><div>C.M. van Duijn</div><div>W.M.M. Verschuren</div><div>C. Wijmenga</div><div>B. Winkelmann</div><div>S. Wyatt</div><div>J.C. Young</div><div>B.O. Boehm</div><div>M. Caulfield</div><div>D.I. Chasman</div><div>K.W. Davidson</div><div>P.A. Doevendans</div><div>E.V.K. FitzGerald</div><div>J.G. Gums</div><div>H. Hakonarson</div><div>H.L. Hillege</div><div>T. Illig</div><div>G.P. Jarvik</div><div>J.A. Johnson</div><div>J.J.P. Kastelein</div><div>W. Koenig</div><div>L.C. Study</div><div>W. März</div><div>B.D. Mitchell</div><div>S.S. Murray</div><div>A.J. Oldehinkel</div><div>D.J. Rader</div><div>M.P. Reilly</div><div>A. Reiner</div><div>E.E. Schadt</div><div>R.L. Silverstein</div><div>H. Snieder</div><div>A. Stanton</div><div>A.G. Uitterlinden</div><div>P. van der Harst</div><div>Y.T. Schouw</div><div>N.J. Samani</div><div>A.D. Johnson</div><div>P. Munroe</div><div>P.I.W. de Bakker</div><div>X. Zhu</div><div>D. Levy</div><div>J. Keating</div><div>F.W. Asselbergs</div>
Blood pressure (BP) is a heritable determinant of risk for cardiovascular disease (CVD). To investigate genetic associations with systolic BP (SBP), diastolic BP (DBP), mean arterial pressure (MAP) and pulse pressure (PP), we genotyped ~50 000 single-nucleotide polymorphisms (SNPs) that capture variation in ~2100 candidate genes for cardiovascular phenotypes in 61 619 individuals of European ancestry from cohort studies in the USA and Europe. We identified novel associations between rs347591 and SBP (chromosome 3p25.3, in an intron of HRH1) and between rs2169137 and DBP (chromosome1q32.1 in an intron of MDM4) and between rs2014408 and SBP (chromosome 11p15 in an intron of SOX6), previously reported to be associated with MAP. We also confirmed 10 previously known loci associated with SBP, DBP, MAP or PP (ADRB1, ATP2B1, SH2B3/ATXN2, CSK, CYP17A1, FURIN, HFE, LSP1, MTHFR, SOX6) at array-wide significance (P < 2.4 × 10-6). We then replicated these associations in an independent set of 65 886 individuals of European ancestry. The findings from expression QTL (eQTL) analysis showed associations of SNPs in the MDM4 region with MDM4 expression. We did not find any evidence of association of the two novel SNPs in MDM4 and HRH1 with sequelae of high BP including coronary artery disease (CAD), left ventricular hypertrophy (LVH) or stroke. In summary, we identified two novel loci associated with BP and confirmed multiple previously reported associations. Our findings extend our understanding of genes involved in BP regulation, some of which may eventually provide new targets for therapeutic intervention.The TRacking Adolescents' Individual Lives Survey (TRAILS): Design, current status, and selected findingshttp://repub.eur.nl/pub/69298/
Mon, 01 Oct 2012 00:00:01 GMT<div>J. Ormel</div><div>A.J. Oldehinkel</div><div>S.J. Sijtsema</div><div>F.V.A. van Oort</div><div>D. Raven</div><div>R. Veenstra</div><div>W.A.M. Vollebergh</div><div>F.C. Verhulst</div>
Objectives: The objectives of this study were as follows: to present a concise overview of the sample, outcomes, determinants, non-response and attrition of the ongoing TRacking Adolescents' Individual Lives Survey (TRAILS), which started in 2001; to summarize a selection of recent findings on continuity, discontinuity, risk, and protective factors of mental health problems; and to document the development of psychopathology during adolescence, focusing on whether the increase of problem behavior often seen in adolescence is a general phenomenon or more prevalent in vulnerable teens, thereby giving rise to diverging developmental pathways. Method: The first and second objectives were achieved using descriptive statistics and selective review of previous TRAILS publications; and the third objective by analyzing longitudinal data on internalizing and externalizing problems using Linear Mixed Models (LMM). Results: The LMM analyses supported the notion of diverging pathways for rule-breaking behaviors but not for anxiety, depression, or aggression. Overall, rule-breaking (in both genders) and withdrawn/depressed behavior (in girls) increased, whereas aggression and anxious/depressed behavior decreased during adolescence. Conclusions: TRAILS has produced a wealth of data and has contributed substantially to our understanding of mental health problems and social development during adolescence. Future waves will expand this database into adulthood. The typical development of problem behaviors in adolescence differs considerably across both problem dimensions and gender. Developmental pathways during adolescence suggest accumulation of risk (i.e., diverging pathways) for rule-breaking behavior. However, those of anxiety, depression and aggression slightly converge, suggesting the influence of counter-forces and changes in risk unrelated to initial problem levels and underlying vulnerability.Puzzling Findings in Studying the Outcome of "Real World" Adolescent Mental Health Services: The TRAILS Studyhttp://repub.eur.nl/pub/38685/
Wed, 19 Sep 2012 00:00:01 GMT<div>F. Jörg</div><div>J. Ormel</div><div>S.A. Reijneveld</div><div>D.E.M.C. Jansen</div><div>F.C. Verhulst</div><div>A.J. Oldehinkel</div>
Background: The increased use and costs of specialist child and adolescent mental health services (MHS) urge us to assess the effectiveness of these services. The aim of this paper is to compare the course of emotional and behavioural problems in adolescents with and without MHS use in a naturalistic setting. Method and Findings: Participants are 2230 (pre)adolescents that enrolled in a prospective cohort study, the TRacking Adolescents' Individual Lives Survey (TRAILS). Response rate was 76%, mean age at baseline 11.09 (SD 0.56), 50.8% girls. We used data from the first three assessment waves, covering a six year period. Multiple linear regression analysis, propensity score matching, and data validation were used to compare the course of emotional and behavioural problems of adolescents with and without MHS use. The association between MHS and follow-up problem score (β 0.20, SE 0.03, p-value<0.001) was not confounded by baseline severity, markers of adolescent vulnerability or resilience nor stressful life events. The propensity score matching strategy revealed that follow-up problem scores of non-MHS-users decreased while the problem scores of MHS users remained high. When taking into account future MHS (non)use, it appeared that problem scores decreased with limited MHS use, albeit not as much as without any MHS use, and that problem scores with continuous MHS use remained high. Data validation showed that using a different outcome measure, multiple assessment waves and multiple imputation of missing values did not alter the results. A limitation of the study is that, although we know what type of MHS participants used, and during which period, we lack information on the duration of the treatment. Conclusions: The benefits of MHS are questionable. Replication studies should reveal whether a critical examination of everyday care is necessary or an artefact is responsible for these results. Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathwayshttp://repub.eur.nl/pub/58771/
Sat, 01 Sep 2012 00:00:01 GMT<div>R.A. Scott</div><div>V. Lagou</div><div>R.P. Welch</div><div>E. Wheeler</div><div>M.E. Montasser</div><div>J. Luan</div><div>R. Mägi</div><div>R.J. Strawbridge</div><div>E. Rehnberg</div><div>S. Gustafsson</div><div>S. Kanoni</div><div>L.J. Rasmussen-Torvik</div><div>L. Yengo</div><div>C. Lecoeur</div><div>D. Shungin</div><div>S. Sanna</div><div>C. Sidore</div><div>P.C.D. Johnson</div><div>J.W. Jukema</div><div>T. Johnson</div><div>A. Mahajan</div><div>N. Verweij</div><div>G. Thorleifsson</div><div>J.J. Hottenga</div><div>S. Shah</div><div>G.D. Smith</div><div>B. Sennblad</div><div>C. Gieger</div><div>P. Salo</div><div>M. Perola</div><div>N. Timpson</div><div>D.M. Evans</div><div>B.S. Pourcain</div><div>Y. Wu</div><div>J.S. Andrews</div><div>J. Hui</div><div>L.F. Bielak</div><div>W. Zhao</div><div>M. Horikoshi</div><div>P. Navarro</div><div>A.J. Isaacs</div><div>J.R. O´Connell</div><div>K. Stirrups</div><div>V. Vitart</div><div>C. Hayward</div><div>T. Esko</div><div>E. Mihailov</div><div>R.M. Fraser</div><div>M. Fall</div><div>B.F. Voight</div><div>S. Raychaudhuri</div><div>H. Chen</div><div>C.M. Lindgren</div><div>A.P. Morris</div><div>N.W. Rayner</div><div>N.R. Robertson</div><div>D. Rybin</div><div>C.-T. Liu</div><div>J.S. Beckmann</div><div>S.M. Willems</div><div>P.S. Chines</div><div>A.U. Jackson</div><div>H.M. Kang</div><div>H.M. Stringham</div><div>K. Song</div><div>T. Tanaka</div><div>J. Peden</div><div>A. Goel</div><div>A.A. Hicks</div><div>P. An</div><div>M. Müller-Nurasyid</div><div>A. Franco-Cereceda</div><div>L. Folkersen</div><div>L. Marullo</div><div>H. Jansen</div><div>A.J. Oldehinkel</div><div>M. Bruinenberg</div><div>J.S. Pankow</div><div>K.E. North</div><div>N.G. Forouhi</div><div>R.J.F. Loos</div><div>T. Edkins</div><div>T.V. Varga</div><div>G. Hallmans</div><div>H. Oksa</div><div>M. Antonella</div><div>R. Nagaraja</div><div>S. Trompet</div><div>I. Ford</div><div>S.J.L. Bakker</div><div>A. Kong</div><div>M. Kumari</div><div>B. Gigante</div><div>C. Herder</div><div>P. Munroe</div><div>M. Caulfield</div><div>J. Antti</div><div>M. Mangino</div><div>K.S. Small</div><div>I. Miljkovic</div><div>Y. Liu</div><div>M. Atalay</div><div>R. Kiess</div><div>A.L. James</div><div>F. Rivadeneira Ramirez</div><div>A.G. Uitterlinden</div><div>C.N.A. Palmer</div><div>A.S.F. Doney</div><div>G.A.H.M. Willemsen</div><div>G.D. Smith</div><div>S. Campbell</div><div>O. Polasek</div><div>L.L. Bonnycastle</div><div>S. Hercberg</div><div>M. Dimitriou</div><div>J.L. Bolton</div><div>G. Fowkes</div><div>P. Kovacs</div><div>J. Lindstrom</div><div>T. Zemunik</div><div>S. Bandinelli</div><div>S.H. Wild</div><div>D.C.G. Basart</div><div>W. Rathmann</div><div>H. Grallert</div><div>W. Maerz</div><div>M.E. Kleber</div><div>B.O. Boehm</div><div>A. Peters</div><div>P.P. Pramstaller</div><div>M.A. Province</div><div>I.B. Borecki</div><div>N. Hastie</div><div>I. Rudan</div><div>H. Campbell</div><div>H. Watkins</div><div>M. Farrall</div><div>M. Stumvoll</div><div>L. Ferrucci</div><div>D. Waterworth</div><div>R.N. Bergman</div><div>F.S. Collins</div><div>J. Tuomilehto</div><div>R.M. Watanabe</div><div>E.J.C. de Geus</div><div>B.W.J.H. Penninx</div><div>A. Hofman</div><div>B.A. Oostra</div><div>B.M. Psaty</div><div>P. Vollenweider</div><div>J.F. Wilson</div><div>A.F. Wright</div><div>G.K. Hovingh</div><div>A. Metspalu</div><div>M. Uusitupa</div><div>P.K. Magnusson</div><div>K.O. Kyvik</div><div>J. Kaprio</div><div>J.F. Price</div><div>G.V. Dedoussis</div><div>P. Deloukas</div><div>P. Meneton</div><div>L. Lind</div><div>M. Boehnke</div><div>A.R. Shuldiner</div><div>C.M. van Duijn</div><div>A.D. Morris</div><div>A. Toenjes</div><div>P.A. Peyser</div><div>J.P. Beilby</div><div>A. KöRner</div><div>J. Kuusisto</div><div>M. Laakso</div><div>S.R. Bornstein</div><div>P.E.H. Schwarz</div><div>T.A. Lakka</div><div>R. Rauramaa</div><div>L.S. Adair</div><div>G.D. Smith</div><div>T.D. Spector</div><div>T. Illig</div><div>U. de Faire</div><div>A. Hamsten</div><div>V. Gudnason</div><div>M. Kivimaki</div><div>A. Hingorani</div><div>S. Keinanen-Kiukaanniemi</div><div>T. Saaristo</div><div>D.I. Boomsma</div><div>J-A. Zwart</div><div>P. van der Harst</div><div>J. Dupuis</div><div>N.L. Pedersen</div><div>N. Sattar</div><div>T.B. Harris</div><div>F. Cucca</div><div>S. Ripatti</div><div>V. Salomaa</div><div>K.L. Mohlke</div><div>B. Balkau</div><div>P. Froguel</div><div>A. Pouta</div><div>M.R. Jarvelin</div><div>N.J. Wareham</div><div>N. Bouatia-Naji</div><div>M.I. McCarthy</div><div>P.W. Franks</div><div>J.B. Meigs</div><div>T.M. Teslovich</div><div>J.C. Florez</div><div>C. Langenberg</div><div>E. Ingelsson</div><div>I. Prokopenko</div><div>I. Barroso</div>
Through genome-wide association meta-analyses of up to 133,010 individuals of European ancestry without diabetes, including individuals newly genotyped using the Metabochip, we have increased the number of confirmed loci influencing glycemic traits to 53, of which 33 also increase type 2 diabetes risk (q < 0.05). Loci influencing fasting insulin concentration showed association with lipid levels and fat distribution, suggesting impact on insulin resistance. Gene-based analyses identified further biologically plausible loci, suggesting that additional loci beyond those reaching genome-wide significance are likely to represent real associations. This conclusion is supported by an excess of directionally consistent and nominally significant signals between discovery and follow-up studies. Functional analysis of these newly discovered loci will further improve our understanding of glycemic control.Timing matters: Long term effects of adversities from prenatal period up to adolescence on adolescents' cortisol stress response. The TRAILS studyhttp://repub.eur.nl/pub/72617/
Sat, 01 Sep 2012 00:00:01 GMT<div>N.M. Bosch</div><div>H. Riese</div><div>S.A. Reijneveld</div><div>M.P. Bakker</div><div>F.C. Verhulst</div><div>J. Ormel</div><div>A.J. Oldehinkel</div>
Objective: Altered cortisol response is a vulnerability marker for a variety of stress-related diseases and psychiatric disorders. Childhood adversity has been shown to modify this response, but evidence is inconsistent. Effects may differ depending on the timing of exposure, or due to the interplay between pre/postnatal adversity and later adversities. The present study examined the influence of adversity during different timeframes (pre/postnatal, ages 0-5, 6-11, 12-13, 14-15 years), and the interaction between pre/postnatal and later adversity on adolescents' cortisol stress response. Method: Four salivary cortisol samples were collected before and after a social stress test in 471 16-year-old adolescents from the longitudinal study TRAILS. Data on pre/postnatal exposure to adversities were obtained from Preventive Child Healthcare records and parental reports, subsequent adversities from parental and self-reports. Results: Pre/postnatal adversity was associated with increased cortisol reactivity. Adversities during ages 0-5 were not associated with cortisol outcomes. Adversities during ages 6-11 were associated with a high cortisol level, especially in those exposed to pre/postnatal adversity, while adversities during ages 12-13 and 14-15 were associated with a low cortisol level. Conclusions: Results highlight the importance to take the timing of stress exposure into account. In addition to programming effects, pre/postnatal adversity interacts with childhood adversity in producing deviant cortisol levels. Puberty may be marked by a transition in how adversities affect the HPA-axis, with cortisol hypersecretion before age 11 and hyposecretion after age 11.Benefits of extensive recruitment effort persist during follow-ups and are consistent across age group and survey method. the TRAILS studyhttp://repub.eur.nl/pub/59858/
Wed, 04 Jul 2012 00:00:01 GMT<div>E. Nederhof</div><div>F. Jörg</div><div>D. Raven</div><div>R. Veenstra</div><div>F.C. Verhulst</div><div>J. Ormel</div><div>A.J. Oldehinkel</div>
Background: Extensive recruitment effort at baseline increases representativeness of study populations by decreasing non-response and associated bias. First, it is not known to what extent increased attrition occurs during subsequent measurement waves among subjects who were hard-to-recruit at baseline and what characteristics the hard-to-recruit dropouts have compared to the hard-to-recruit retainers. Second, it is unknown whether characteristics of hard-to-recruit responders in a prospective population based cohort study are similar across age group and survey method. Methods. First, we compared first wave (T1) easy-to-recruit with hard-to-recruit responders of the TRacking Adolescents' Individual Lives Survey (TRAILS), a prospective population based cohort study of Dutch (pre)adolescents (at first wave: n = 2230, mean age = 11.09 (SD 0.56), 50.8% girls), with regard to response rates at subsequent measurement waves. Second, easy-to-recruit and hard-to-recruit participants at the fourth TRAILS measurement wave (n = 1881, mean age = 19.1 (SD 0.60), 52.3% girls) were compared with fourth wave non-responders and earlier stage drop-outs on family composition, socioeconomic position (SEP), intelligence (IQ), education, sociometric status, substance use, and psychopathology. Results: First, over 60% of the hard-to-recruit responders at the first wave were retained in the sample eight years later at the fourth measurement wave. Hard-to-recruit dropouts did not differ from hard-to-recruit retainers. Second, extensive recruitment efforts for the web based survey convinced a population of nineteen year olds with similar characteristics as the hard-to-recruit eleven year olds that were persuaded to participate in a school-based survey. Some characteristics associated with being hard-to-recruit (as compared to being easy-to-recruit) were more pronounced among non-responders, resembling the baseline situation (De Winter et al.2005). Conclusions: First, extensive recruitment effort at the first assessment wave of a prospective population based cohort study has long lasting positive effects. Second, characteristics of hard-to-recruit responders are largely consistent across age groups and survey methods.Identifying target groups for the prevention of depression in early adolescence: The TRAILS studyhttp://repub.eur.nl/pub/70262/
Tue, 01 May 2012 00:00:01 GMT<div>K. Monshouwer</div><div>F. Smit</div><div>M. Ruiter</div><div>J. Ormel</div><div>F.C. Verhulst</div><div>W.A.M. Vollebergh</div><div>A.J. Oldehinkel</div>
Background: Depression in adolescence is associated with long-term adverse consequences. The aim of the present study is to identify target groups at increased risk of developing depression in early adolescence, such that prevention is associated with the largest health benefit at population-level for the least effort. Methods: The analyses were conducted on data of the first (age range 10-12) and fourth (age range 17-20) wave of a population-based cohort study (N = 1538). The Composite International Diagnostic Interview (CIDI) was used to assess onset of major depression in early adolescence. High-risk groups were identified using exposure rate, incidence rate and population attributable fraction. Results: Prevention of depression onset in early adolescence is best targeted at children with one of the following risk profiles: a high body mass index in combination with (1) maternal depression (2) female gender, and (3) parental emotional rejection. Limitations: Age of onset of depression was assessed retrospectively. Conclusions: Only a few risk indicators are needed to identify a relatively small group which accounts for a substantial percentage of the new cases of depression in early adolescence.Childhood Family Instability and Mental Health Problems During Late Adolescence: A Test of Two Mediation Models-The TRAILS Studyhttp://repub.eur.nl/pub/56318/
Thu, 01 Mar 2012 00:00:01 GMT<div>M.P. Bakker</div><div>J. Ormel</div><div>F.C. Verhulst</div><div>A.J. Oldehinkel</div>
This study tested whether childhood family instability is associated with mental health problems during adolescence through continued family instability and/or through a preadolescent onset of mental health problems. This test use data from a prospective population cohort of 2,230 Dutch adolescents (M age = 11.09, SD = 0.56 at the initial assessment). Childhood family instability was associated with both internalizing problems and externalizing problems during late adolescence. The association between childhood family instability and adolescent mental health problems largely disappeared when controlling for preadolescent onset of mental health problems but only slightly when controlling for continued family instability during adolescence. These patterns were comparable for both types of mental health problems but relatively stronger for internalizing problems. These results suggest that growing up in an unpredictable family environment has long-lasting negative mental health effects, most of which are due to a preadolescent onset of mental health problems.Symptom-specific associations between low cortisol responses and functional somatic symptoms: The TRAILS studyhttp://repub.eur.nl/pub/65343/
Thu, 01 Mar 2012 00:00:01 GMT<div>K.A.M. Janssens</div><div>A.J. Oldehinkel</div><div>F.C. Verhulst</div><div>J.A.M. Hunfeld</div><div>J. Ormel</div><div>J.G.M. Rosmalen</div>
Background: Functional somatic symptoms (FSS), like chronic pain and overtiredness, are often assumed to be stress-related. Altered levels of the stress hormone cortisol could explain the association between stress and somatic complaints. We hypothesized that low cortisol levels after awakening and low cortisol levels during stress are differentially associated with specific FSS. Methods: This study is performed in a subsample of TRAILS (Tracking Adolescents' Individual Lives Survey) consisting of 715 adolescents (mean age: 16.1. years, SD. = 0.6, 51.3% girls). Adolescents' cortisol levels after awakening and during a social stress task were assessed. The area under the curve with respect to the ground (AUCg) and the area under the curve above the baseline (AUCab) were calculated for these cortisol levels. FSS were measured using the Youth Self-Report and pain questions. Based upon a factor analysis, FSS were divided into two clusters, one consisting of headache and gastrointestinal symptoms and the other consisting of overtiredness, dizziness and musculoskeletal pain. Results: Regression analyses revealed that the cluster of headache and gastrointestinal symptoms was associated with a low AUCg of cortisol levels during stress (β=-.09, p=.03) and the cluster of overtiredness, dizziness and musculoskeletal pain with a low AUCg of cortisol levels after awakening (β= -.15, p=.008). All these analyses were adjusted for the potential confounders smoking, physical activity level, depression, corticosteroid use, oral contraceptive use, gender, body mass index and, if applicable, awakening time. Conclusion: Two clusters of FSS are differentially associated with the stress hormone cortisol.Prenatal smoking exposure and the risk of behavioral problems and substance use in adolescence: The TRAILS studyhttp://repub.eur.nl/pub/34127/
Thu, 01 Dec 2011 00:00:01 GMT<div>K. Monshouwer</div><div>A.C. Huizink</div><div>Z. Harakeh</div><div>Q.A.W. Raaijmakers</div><div>S.A. Reijneveld</div><div>A.J. Oldehinkel</div><div>F.C. Verhulst</div><div>W.A.M. Vollebergh</div>
Aims: To study the prospective relationship between maternal smoking during pregnancy (MSP) and behavioral problems, heavy alcohol use, daily smoking, and ever use of cannabis in the offspring, and to assess the role of confounding and mediating factors in a systematic way. Methods: Population-based cohort study of 2,230 respondents, starting in 2001 when respondents were around the age of 11 years, and two follow-up measurements at intervals of about 2.5 years (response rates of 96.0 and 81.4%). Results: Almost one third of the respondents' mothers had smoked tobacco during pregnancy. These respondents were at an increased risk for all outcomes except internalizing problems (significant odds ratios ranged from 1.40 to 2.97). The successive models showed that the potential confounding factors reduced the strength of all relationships. In the full model, the strongest relationship was found for mothers who smoked more than 10 cigarettes a day during pregnancy and daily smoking in early adolescence (odds ratio: 1.56), but none of the relationships were statistically significant. Conclusions: MSP is a marker for future behavioral outcomes in the offspring, but reducing the prevalence of MSP is unlikely to make a meaningful contribution to the prevention of these problems in adolescents. Copyright